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Enamine
AI-Driven In-Silico Drug Discovery for Molecule Library
estimated R&D cost savings
reduction in required wet-lab experiments
time saved in hit identification
Accelerating Hit Identification with Machine Learning and Virtual Screening
See TestimonialBiotech
Industry
USA
Location
Drug discovery, AI compound screening, chemical space optimization
Services
Enamine, a global leader in compound libraries, needed a faster, scalable alternative to high-throughput screening (HTS) for early-stage drug discovery.
See what we can do for youTo develop a scalable AI-driven pipeline, Blackthorn AI applied:
Project duration
01 Week
Integrated 20K labeled molecules (hits, non-binders); accessed 36B Enamine REAL DB via FTP tranches and parsed bz2 files for enumeration.
02 Week
Trained ML model to predict ligand-target binding affinity. Validated results against HTS-confirmed hits and selective binders.
03 Week
Scored billions of molecules; selected top 100K candidates with highest predicted affinity. Prepped inputs for docking.
04 Week
Ran DiffDock on 1M+ ligands. Mapped binding pocket coverage and exported top 10K hits for lab validation and downstream FEP modeling.
Team Size
Delivering Impact
99.99%
reduction in screening spaceFrom 36B to 10K molecules using virtual screening — cutting physical screening needs by 3.6M times
$10M+
estimated R&D cost savingsAvoided synthesis/screening of millions of compounds (avg. $500–$1,000 per compound)
6–12 month
month savedHit ID accelerated from year-long HTS workflows to <8 weeks
>80%
fewer wet-lab experiments requiredLab work focused on just 0.0003% of initial chemical space
